18 citations · 28 across the 16 of their papers we have counts for
10 papers · 1 filter
OnlyFlow: Optical Flow based Motion Conditioning for Video Diffusion Models
Mathis Koroglu, Hugo Caselles-Dupré, Guillaume Jeanneret Sanmiguel +1
We consider the problem of text-to-video generation tasks with precise control for various applications such as camera movement control and video-to-video editing. Most methods tac…
Skipping Computations in Multimodal LLMs
Mustafa Shukor, Matthieu Cord
Large Language Models (LLMs) have demonstrated remarkable success in both textual and multimodal domains. However, this success often comes with substantial computational costs, pa…
A Concept-Based Explainability Framework for Large Multimodal Models
Jayneel Parekh, Pegah Khayatan, Mustafa Shukor +2
Large multimodal models (LMMs) combine unimodal encoders and large language models (LLMs) to perform multimodal tasks. Despite recent advancements towards the interpretability of t…
DiffCut: Catalyzing Zero-Shot Semantic Segmentation with Diffusion Features and Recursive Normalized Cut
Paul Couairon, Mustafa Shukor, Jean-Emmanuel Haugeard +2
Foundation models have emerged as powerful tools across various domains including language, vision, and multimodal tasks. While prior works have addressed unsupervised image segmen…
Mind-to-Image: Projecting Visual Mental Imagination of the Brain from fMRI
Hugo Caselles-Dupré, Charles Mellerio, Paul Hérent +6
The reconstruction of images observed by subjects from fMRI data collected during visual stimuli has made strong progress in the past decade, thanks to the availability of extensiv…
What matters when building vision-language models?
Hugo Laurençon, Léo Tronchon, Matthieu Cord +1
The growing interest in vision-language models (VLMs) has been driven by improvements in large language models and vision transformers. Despite the abundance of literature on this…
Implicit Multimodal Alignment: On the Generalization of Frozen LLMs to Multimodal Inputs
Mustafa Shukor, Matthieu Cord
Large Language Models (LLMs) have demonstrated impressive performance on multimodal tasks, without any multimodal finetuning. They are the building block for Large Multimodal Model…
What Makes Multimodal In-Context Learning Work?
Folco Bertini Baldassini, Mustafa Shukor, Matthieu Cord +2
Large Language Models have demonstrated remarkable performance across various tasks, exhibiting the capacity to swiftly acquire new skills, such as through In-Context Learning (ICL…
Improved Baselines for Data-efficient Perceptual Augmentation of LLMs
Théophane Vallaeys, Mustafa Shukor, Matthieu Cord +1
The abilities of large language models (LLMs) have recently progressed to unprecedented levels, paving the way to novel applications in a wide variety of areas. In computer vision,…
FreeSeg-Diff: Training-Free Open-Vocabulary Segmentation with Diffusion Models
Barbara Toniella Corradini, Mustafa Shukor, Paul Couairon +3
Foundation models have exhibited unprecedented capabilities in tackling many domains and tasks. Models such as CLIP are currently widely used to bridge cross-modal representations,…